Turning Information Into Action

Gathering information, however, is only half the story. You then need to be able to use it effectively to support your decision-making.

The first step towards this is to assess the information’s quality and decide what you are going to use. The final step is to use your chosen data to inform your decision-making.

This page explains these two final parts of the jigsaw.

Assessing The Information

Our page on Market Research and Competitive Intelligence explains the information funnel. At the top, it is wide, and you can feed in lots of data and information of all kinds and from a wide range of sources, without worrying about its quality. As you move through the funnel, however, you start to assess the information and potentially discard some.

There are a number of aspects that you can consider in assessing information quality (see box).

Wang and Strong’s Dimensions of Data Quality

Wang and Strong (1996) identified four dimensions of data quality that should be assessed. These were:

Intrinsic: characteristics of the data itself, such as its objectivity;

Contextual: considering the data within the context of its planned use;

Representational and Accessibility: considering the data within the system, and including characteristics like compatibility with other data, and ease of access.

It is helpful to use a framework like Wang and Strong’s proposed dimensions when assessing information quality, as it ensures that you do not forget particular aspects.

Intrinsic characteristics

These include reliability, objectivity, believability, and the reputation of the source. Questions to ask include:

Can I check the accuracy of this information?

For example, are there other sources that might provide corroboration? If five different news sources are reporting the same information, that makes it more likely to be accurate. Company reports and financial information that are required to be published should also be accurate, although be aware that companies will only publish what is necessary.

Who is the author or source of this information? Are they objective, or do they have a potential bias of some sort?

For example, newspapers all have a certain amount of bias: they reflect the views of their owner and/or editor, and they want people to read their stories (because that sells advertising). Similarly, ex-employees will know about a company but, if they have left under less positive circumstances, they may have a reason to have a negative view of the company.

What is the reputation of the source?

The reputation of the source can also tell you a lot: for example, is that news source generally considered reliable? If the information is from a personal contact, are they generally someone who knows what is going on, or something of a gossip?

Is this information believable?

For example, if you read that Angelina Jolie was planning to adopt another child, that might not be accurate, but it would be believable based on her past actions. If, however, you read that she had declared that she was a cat, this would be less believable given her reputation as a serious campaigner for human rights.

Contextual characteristics

These tend to assess the usefulness of the information: in other words, will be it be helpful in the way that you want to use it? Questions to ask include:

Is this information up-to-date?

Information definitely has a sell-by date, but it also has a half-life. In other words, as it becomes older, it becomes less useful, because it is less likely to be accurate. You should always try to use the most up-to-date information you can find, even if it is slightly less than perfect in other ways.

Is the information relevant? In other words, does it answer the right question?

It does not matter whether data is timely and accurate if it does not answer the right question. Near enough may be good enough, but it may not be, too.

Is the information complete?

This is particularly important for survey information: you need to check whether there is a lot of missing data. If so, it will be less reliable.

Contextual characteristics are particularly sensitive to your circumstances. It is therefore important that they reflect the way in which you want to use the information.

Representational and Accessibility characteristics

These characteristics look at the information within the system, to make sure that it fits with the wider context, as well as the task in hand. Questions to ask include:

Is the information compatible with data from other sources?

If you want to do any statistical analysis, you need to make sure that you are comparing like with like. There is more about this in our pages on statistical analysis, starting with Simple Statistical Analysis.

How easy is it to get the information?

For example, if it is already collected by your organisation as standard, that is very easy to access. However, data or information that is not already collected may need to be commissioned, either as a one-off research project, or a new collection, and this will take time and money. You will need to consider whether it is worth the effort and expense.

Is the information easy to interpret?

Depending on your background, and the background of your proposed audience, some information will be much easier to understand than others. You need to ensure that you do not ‘blind anyone with science’, or confuse yourself.

Using Information to Support Decisions

Once you have your information, and you know that it is high enough quality for your purposes, the final step is to use it to support your decision-making.

On an individual basis, this is relatively easy: you just have to decide that is what you are going to do.

The problem comes if you are trying to do this on an organisational basis, because you may be fighting against an established organisational culture. You may need to take the process in small steps, demonstrating how useful information and analysis can be in small ways, before it is routinely relied on for major projects and decisions.

Data vs. ‘gut feeling’

It is always going to be difficult if your analysis presents you with a result that your ‘gut feeling’ says is wrong.

Under these circumstances, it is wise not to ignore either. Instead, you probably need to look for more information to confirm one or other position.

Don’t assume that your data is correct, simply because it contains numbers. There may be a fault in either your data or your analysis. Check both carefully, and look for other information that could change your analysis.

Equally, don’t assume that your gut feeling must be right. Ask yourself why you think that, and then look for something that would challenge your thinking.

Just do it…

The key with using information to support decision-making is simple: just do it.

As you gather more data and information, and use it every day, it will become more routine. You will soon start to see the benefits of decisions driven by accurate information, rather than anecdote and gut feeling.

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